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Refactor all functions
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@sdgamboa
sdgamboa committed Jan 13, 2025
1 parent 1b2471b commit 050e15d
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Package: MicrobiomeBenchmarkDataAnalyses
Title: Benchmarking analyses with the MicrobiomeBenchmarkData package
Version: 0.99.21
Version: 0.99.22
Authors@R:
person("Samuel David", "Gamboa-Tuz", email = "[email protected]",
role = c("aut", "cre"), comment = c(ORCID = "0000-0002-6863-7943"))
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10 changes: 1 addition & 9 deletions NAMESPACE
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Expand Up @@ -6,17 +6,9 @@ export(DA_lefse)
export(DA_wilcox)
export(editMiaTaxaNames)
export(filterTaxa)
export(getMethodClass)
export(get_direction_cols)
export(get_meth_class)
export(norm_clr)
export(norm_tss)
export(plot_enrichment)
export(plot_enrichment_2)
export(plot_positives)
export(quiet)
export(run_DA)
export(set_DA_methods_list)
export(set_norm_list)
export(taxize_classification_to_taxonomy_table)
importFrom(dplyr,bind_rows)
importFrom(magrittr,"%>%")
6 changes: 3 additions & 3 deletions R/MicrobiomeBenchmarkDataAnalyses.R
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#' MicrobiomeBenchmarkDataAnalyses
#'
#' \code{MicrobiomeBenchmarkDataAnalyses} is a package for the DA analyses
#' performed on the MicrobiomeBenchmarkData package.
#' \code{MicrobiomeBenchmarkDataAnalyses} contains the code used
#' to analyze the microbiome datasets in the \code{MicrobiomeBenchmarkData}
#' package.
#'
#' @name MicrobiomeBenchmarkDataAnalyses
#' @importFrom magrittr %>%
#'
NULL
2 changes: 1 addition & 1 deletion R/new_DA_methods.R
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Expand Up @@ -504,7 +504,7 @@ DA_ancombc <- function(
## Perform analysis with ancombc
phyloseq::otu_table(object) <- counts ## replace otu table
res <- ANCOMBC::ancombc(
phyloseq = object, group = group, formula = formula, ...
data = object, group = group, formula = formula, ...
)[['res']]

# Create pValMat and statInfo
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64 changes: 0 additions & 64 deletions R/run_DA.R
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174 changes: 50 additions & 124 deletions R/set_DA_methods_list.R
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Expand Up @@ -16,196 +16,123 @@
#'
set_DA_methods_list <- function(conditions_col, conditions) {
c(
# edgeR
my_edger <- benchdamic::set_edgeR(
edgeR.TMM = benchdamic::set_edgeR(
group_name = conditions_col,
design = stats::as.formula(paste0("~", conditions_col)),
norm = "TMM",
coef = 2,
weights_logical = FALSE
),

# edgeR + weights
my_edger <- benchdamic::set_edgeR(
) |>
unname(),
edgeR.TMM.weighted = benchdamic::set_edgeR(
group_name = conditions_col,
design = stats::as.formula(paste0("~", conditions_col)),
norm = "TMM",
coef = 2,
weights_logical = TRUE
),

# DESeq2
my_deseq2 <- benchdamic::set_DESeq2(
) |>
unname(),
DESeq2.poscounts = benchdamic::set_DESeq2(
contrast = c(conditions_col, conditions['condA'], conditions['condB']),
design = stats::as.formula(paste0("~", conditions_col)),
norm = "poscounts",
weights_logical = FALSE
),

# DESeq2 + weights
my_deseq2 <- benchdamic::set_DESeq2(
) |>
unname(),
DESeq2.poscounts.weighted = benchdamic::set_DESeq2(
contrast = c(conditions_col, conditions['condA'], conditions['condB']),
design = stats::as.formula(paste0("~", conditions_col)),
norm = "poscounts",
weights_logical = TRUE
),

# limma
my_limma <- benchdamic::set_limma( # I get a warning
) |>
unname(),
limma.TMM = benchdamic::set_limma(
design = stats::as.formula(paste0("~", conditions_col)),
norm = "TMM",
coef = 2,
weights_logical = FALSE
),

# limma + weights
my_limma <- benchdamic::set_limma(
) |>
unname(),
limma.TMM.weighted = benchdamic::set_limma(
design = stats::as.formula(paste0("~", conditions_col)),
norm = "TMM",
coef = 2,
weights_logical = TRUE
),

## metagenomeSeq
my_metagenomeseq <- benchdamic::set_metagenomeSeq(
) |>
unname(),
metagenomeSeq.CSS = benchdamic::set_metagenomeSeq(
design = stats::as.formula(paste0("~", conditions_col)),
norm = c("CSS"),
coef = 2
),

## ALDEx2 with t-test
# my_aldex2 <- benchdamic::set_ALDEx2(
# conditions = conditions_col,
# test = "t",
# norm = "none"
# ),

## ALDEx2 with wilcox
my_aldex2 <- benchdamic::set_ALDEx2(
# conditions = conditions_col,
) |>
unname(),
ALDEx2 = benchdamic::set_ALDEx2(
contrast = c(conditions_col, conditions[[2]], conditions[[1]]),
test = "wilcox",
design = conditions_col
# norm = "none"
),

## corncob Corncob stopped working and was removed from benchdamic (temporarily maybe)
# my_corncob <- benchdamic::set_corncob(
# formula = stats::as.formula(paste0("~", conditions_col)),
# phi.formula = stats::as.formula(paste0("~", conditions_col)),
# formula_null = ~ 1,
# phi.formula_null = stats::as.formula(paste0("~", conditions_col)),
# test = "Wald",
# coefficient = paste0(conditions_col, conditions['condA']),
# norm = "none"
# ),

## MAST
my_mast <- benchdamic::set_MAST(
) |>
unname(),
MAST = benchdamic::set_MAST(
rescale = "median",
design = stats::as.formula(paste0("~", conditions_col)),
coefficient = paste0(conditions_col, conditions['condA'])
# norm = "none"
),

## Seurat
my_seurat <- benchdamic::set_Seurat(
) |>
unname(),
Seurat = benchdamic::set_Seurat(
test = "wilcox",
contrast = c(conditions_col, conditions['condA'], conditions['condB']),
# contrast = NULL,
norm = "none"
),

new_methods <- list(
## ANCOMB-BC
ancombc.1 = list(
method = 'DA_ancombc',
conditions = conditions,
group = conditions_col,
formula = conditions_col,
norm = 'none',
p_adj_method = 'fdr'
) |>
unname(),
list(
ANCOMBC = list(
method = 'DA_ancombc',
conditions = conditions,
group = conditions_col,
formula = conditions_col,
norm = 'none',
p_adj_method = 'fdr'
),
## Wilcox
# wilcox.2 = list(
# method = 'DA_wilcox',
# norm = 'none',
# conditions_col = conditions_col,
# conditions = conditions
# ),
wilcox.3 = list(
Wilcox.TSS = list(
method = 'DA_wilcox',
norm = 'TSS',
conditions_col = conditions_col,
conditions = conditions
),
wilcox.4 = list(
Wilcox.CLR = list(
method = 'DA_wilcox',
norm = 'CLR',
conditions_col = conditions_col,
conditions = conditions
),
## ZINQ
# ZINQ.5 = list(
# method = 'DA_ZINQ',
# conditions_col = conditions_col,
# conditions = conditions,
# norm = 'none', pval_method = 'MinP', y_CorD = 'D'
# ),
# ZINQ.6 = list(
# method = 'DA_ZINQ',
# conditions_col = conditions_col,
# conditions = conditions,
# norm = 'TSS', pval_method = 'MinP', y_CorD = 'C'
# ),
# ZINQ.7 = list(
# method = 'DA_ZINQ',
# conditions_col = conditions_col,
# conditions = conditions,
# norm = 'CLR', pval_method = 'MinP', y_CorD = 'C'
# ),
# ZINQ.8 = list(
# method = 'DA_ZINQ',
# conditions_col = conditions_col,
# conditions = conditions,
# norm = 'none', pval_method = 'Cauchy', y_CorD = 'D'
# ),
ZINQ.9 = list(
ZINQ.TSS = list(
method = 'DA_ZINQ',
conditions_col = conditions_col,
conditions = conditions,
norm = 'TSS', pval_method = 'Cauchy', y_CorD = 'C'
),
ZINQ.10 = list(
ZINQ.CLR = list(
method = 'DA_ZINQ',
conditions_col = conditions_col,
conditions = conditions,
norm = 'CLR', pval_method = 'Cauchy', y_CorD = 'C'
),
# lefse.11 = list(
# method = 'DA_lefse',
# conditions = conditions,
# norm = 'none',
# groupCol = conditions_col,
# kruskal.threshold = 1,
# wilcox.threshold = 1,
# lda.threshold = 0
# ),

## I really need p-values to help define DA abundant features
## So I'm using a p-value threshold only for the lefse method
lefse.12 = list(
## P-value thresholds are needed here since they are not provided
## in the output of the lefser package
LEfSe.TSS = list(
method = 'DA_lefse',
conditions = conditions,
norm = 'CLR',
norm = 'TSS',
groupCol = conditions_col,
kruskal.threshold = 0.05,
wilcox.threshold = 0.05,
lda.threshold = 0
),
lefse.13 = list(
LEfSe.CLR = list(
method = 'DA_lefse',
conditions = conditions,
norm = 'TSS',
norm = 'CLR',
groupCol = conditions_col,
kruskal.threshold = 0.05,
wilcox.threshold = 0.05,
Expand All @@ -214,5 +141,4 @@ set_DA_methods_list <- function(conditions_col, conditions) {
)

)

}
}
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